While volume-driven industries such as automotive are characterized by a high degree of data backflow across all production cycles, there is still a certain residue in the planning and construction of process plants. This is firstly due to the high proportion of customer-specific requirements and secondly to the significant amount of value added on site during construction. To handle recurring project-specific process plants as time- and cost-efficiently as possible, optimal information exchange among contractors of various disciplines and the plant developer is a prerequisite. For this purpose, a holistic digital representation of the plant is created, which consolidates all relevant information in one place serving as a foundation of multiple digital twins. An approach to identify and define relevant information depending on their subsequent use is developed. On this basis, a framework is proposed to enable a multipliable BOM-based automatic definition of information backflow to instantiate digital representations in parallel to the planning and construction process. Furthermore, project-specific contextual information will be captured and referenced in a structured form preventing their loss for subsequent similar projects.

Answer-Set Programming (ASP) is a powerful logic-based programming language, which is enjoying increasing interest within the scientific community and (very recently) in industry. The evaluation of Answer-Set Programs is traditionally carried out in two steps. At the first step, an input program undergoes the so-called instantiation (or grounding) process, which produces a program ′ semantically equivalent to , but not containing any variable; in turn, ′ is evaluated by using a backtracking search algorithm in the second step. It is well-known that instantiation is important for the efficiency of the whole evaluation, might become a bottleneck in common situations, is crucial in several real-world applications, and is particularly relevant when huge input data have to be dealt with. At the time of this writing, the available instantiator modules are not able to exploit satisfactorily the latest hardware, featuring multi-core/multi-processor Symmetric MultiProcessing technologies. This paper presents some parallel instantiation techniques, including load-balancing and granularity control heuristics, which allow for the effective exploitation of the processing power offered by modern Symmetric MultiProcessing machines. This is confirmed by an extensive experimental analysis reported herein.

This study examined whether Alzheimer patients can make elaborative inferences based on the semantic context provided by a sentence. More specifically, if presented with the name of a category in a sentence do they, like normals, infer (instantiate) the particular member of that category most appropriate to the meaning of the sentence (e.g., if a sentence mentions a container of juice, do they infer it is a bottle). Patients were presented with a sentence containing the name of a concrete category. The sense of the sentence was consistent with a low-dominant member of that category. Patients were then shown drawings of four members of that category and asked to select the one appropriate to the sentence. They were later asked to name the drawings. If semantic information is degraded in Alzheimer patients for those objects Alzheimer patients cannot name (as has been claimed), then AD patients should be unable to carry out the type of elaborative semantic inference required to instantiate. Results showed that Alzheimer patients were highly accurate at instantiating even objects they could not name. This is consistent with a relative preservation of semantic knowledge about concrete objects in Alzheimer patients. (JINS, 1999, 5, 685–691.)